A popular approach to solving large-scale instances of (REF ) is to decompose the problem iteratively into individual block subproblems that are easier to handle numerically.
This decomposition can be done either at the linear algebra level of an interior point method [1]}, [2]}, [3]} or by utilizing (augmented) Lagrangian functions to solve block-separable dual problems that are derived using (local) convex duality [4]}, [5]}, [6]}.
The alternating direction method of multipliers (ADMM) belongs to the latter class of methods and has seen a recent surge in popularity because of its suitability for distributed computation; e.g., see [7]}.
However, the vast majority of provably convergent ADMM approaches for solving (REF ) either exploit convexity in the objective function \(f_t\)  or constraints \(X_t\)  or they are tailored for specific instances of (REF ); e.g., see [8]}, [9]}, [10]}, [11]}, [12]}, [13]}.
